Experimental study of reverse reconstruction for transformer radiation noise based on ARMA model
Tian Haoyang, Peng Xu, Lin He, Xinye Wu · 2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA) · 2022
Based on the acoustic radiation theory and laser vibration measurement method, the surface vibration and radiation noise sound pressure of the dry-type transformer model are measured experimentally, and the frequency response function method and surface vibration velocity method are used to obtain the radiation exponent of the transformer, respectively. On this basis, the autoregressive moving average model (ARMA) is used to study the reverse reconstruction of transformer noise, and the predicted values of sound pressure and sound pressure level are obtained and compared with the experimental values. The results show that the radiation exponent obtained by the frequency response function method is slightly smaller than that of the surface vibration method by about 1~1.5 dB. The difference between the sound pressure obtained by the noise prediction algorithm based on the ARMA model and the experimental test sound pressure is about 0.015 Pa, and the difference in sound pressure level is about 1 dB. The prediction algorithm based on ARMA model and laser vibration measurement for reverse reconstruction of noise has good feasibility and can be applied to prediction of radiated noise of other structures.